ZipRecruiter

Log In

1

Generative Ai Testing Jobs in Delaware (NOW HIRING)

All JobsGenerative Ai Testing JobsGenerative Ai Testing Jobs in Delaware

Generative Ai Testing information

What is the difference between Generative Ai Testing vs Data Scientist?

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Certifications in AI or data science can enhance your qualifications and improve job prospects.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development that involves evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, making it a promising career path with increasing demand as AI technologies expand. Professionals in this area can find opportunities in tech companies, research labs, and startups focused on AI innovation.

What are the key skills and qualifications needed to thrive as a generative AI testing specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What is generative AI testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.
What are popular job titles related to Generative Ai Testing jobs in Delaware? For Generative Ai Testing jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Delaware look for? The top searched job categories for Generative Ai Testing jobs in Delaware are:
What cities in Delaware are hiring for Generative Ai Testing jobs? Cities in Delaware with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Delaware as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 12% Part Time, 5% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

GenAI Engineer - Wilmington, DE, Fairfax, VA, Washington, DC, Silver Spring, MD, Philadelphia, PA

TechniPros, LLC

Wilmington, DE • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Job Title: GenAI Engineer 
Location: Wilmington, DE, Fairfax, VA, Wahington, DC, Silver Spring, MD, Philadelphia, PA
Contact: 12+ Months
Looking for W2 candidates. No C2C

Job Summary
We are seeking a highly skilled GenAI Engineer with hands-on experience in Devin AI, Claude Code, and AI-assisted software engineering to accelerate software development through intelligent automation. The ideal candidate will partner with engineering teams to design, develop, test, and deploy enterprise applications while leveraging AI coding agents to improve productivity, code quality, and delivery speed. This role requires strong software engineering fundamentals, cloud-native development experience, CI/CD expertise, and a passion for building reusable AI engineering assets.

Key Responsibilities
•    Design, develop, test, and deploy enterprise software solutions in collaboration with engineering teams.
•    Leverage Devin AI, Claude Code, GitHub Copilot, or similar AI coding assistants to accelerate software development, testing, debugging, documentation, and modernization initiatives.
•    Create and maintain reusable AI assets including Devin Playbooks, Knowledge Assets, CLAUDE.md files, Skills, Hooks, and workflow templates.
•    Contribute to repository onboarding and AI workflow standardization across engineering teams.
•    Collaborate with architects, product owners, QA teams, and developers to deliver scalable, high-quality software solutions.
•    Ensure AI-generated code complies with engineering best practices, security standards, quality guidelines, and organizational compliance requirements.
•    Continuously evaluate and improve AI-assisted software development workflows and engineering practices.
•    Promote AI engineering best practices and share reusable assets across multiple teams.
•    Support DevOps initiatives, CI/CD automation, and cloud-native application development.
•    Required Qualifications
•    Bachelor''s degree in Computer Science, Software Engineering, or related field.
•    Strong experience in software engineering and enterprise application development.
•    Hands-on experience with Devin AI, Claude Code, GitHub Copilot, or similar AI-assisted development platforms.
•    Strong knowledge of modern software development methodologies and Agile practices.
•    Experience with CI/CD pipelines, automated testing, and cloud-native application development.
•    Experience developing APIs and microservices.
•    Strong understanding of software architecture, design patterns, and engineering best practices.
•    Excellent analytical, communication, and collaboration skills.
•    Passion for AI-assisted engineering and developer productivity improvements.

Preferred Qualifications
•    Experience with Agentic AI development.
•    Experience building Generative AI applications.
•    Knowledge of Retrieval-Augmented Generation (RAG).
•    Experience with Prompt Engineering and Context Engineering.
•    DevOps and Platform Engineering experience.
•    Experience with workflow automation frameworks.
•    Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.

Required Skills
•    Devin AI
•    Claude Code
•    GitHub Copilot
•    Software Engineering
•    CI/CD
•    Cloud-Native Applications
•    API Development
•    Microservices
•    Agile/Scrum
•    DevOps
•    AI-Assisted Development

Nice-to-Have Skills
•    Agentic AI
•    Generative AI
•    RAG
•    Prompt Engineering
•    Context Engineering
•    Workflow Automation
•    Platform Engineering

Best Regards: 

Julia T
Phone:
Email:

Apply